Procedia - Social and Behavioral Sciences 96 ( 2013 ) 231 – 242
1877-0428 © 2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license. Selection and peer-review under responsibility of Chinese Overseas Transportation Association (COTA). doi: 10.1016/j.sbspro.2013.08.030
13th COTA International Conference of Transportation Professionals (CICTP 2013)
Travel Behavior of the Urban Low-Income in China: Case Study
of Huzhou City
* ,Xiaoying BIb
, Xuewu CHENc
, Lei LId
a Graduate Research Assistant, School of Transportation, Southeast University, Sipailou #2, Nanjing, 210096, P. R. China b Graduate Research Assistant, School of Transportation, Southeast University, Sipailou #2, Nanjing, 210096, P. R. China
c Professor, School of Transportation, Southeast University, Sipailou #2, Nanjing, 210096, P. R. China d Graduate Research Assistant, School of Transportation, Southeast University, Sipailou #2, Nanjing, 210096, P. R. China
Research on travel behavior of the urban low-income citizens in China is minimal. The main objective of this study is to examine the travel characteristics and trip chain characteristics of China s urban low-income. Using the travel data collected in the city of Huzhou (a prefecture-level city in east China), Two-Step Clustering Analysis is conducted to identify low-income people from the dataset. It is found that urban low-low-income people have lower mobility than the non-low-low-income. They tend to make fewer trips and of shorter distance. However, average trip duration of the low-income is longer than that of the non-low-income. Results show that the low-income individual makes most of their trips for subsistence activities, such as going to work or school. In addition, large proportions of all trips made by the low-income are undertaken by walking, electric motorcycle, and bicycle. The number of trip chain of low-income people is generally small, and the length of said trip chain is short. Finally policy implications are recommended in the context of providing transportation equity service to improve the mobility of urban low-income.
© 2013 The Authors. Published by Elsevier B.V.
Selection and/or peer-review under responsibility of Chinese Overseas Transportation Association (COTA).
Keywords:Urban low-income; Travel behavior; Two-step clustering; Policy recommendations
Transportation is a critical element for everyone to accomplish tasks in their daily lives, including getting to work and school, and accessing goods and services. Travel behavior of the low-income has been the subject of
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© 2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.
debate in developed countries like America and European countries. However, much remains to be explored about travel behavior of urban low-income in developing countries, especially in China.
In an environment of rapid economic development with rising income, escalating motorization, and growing urbanization, the income gap of Chinese people is widening. Gini coefficient is an important index measuring the inequality among levels of income. According to National Bureau of Statistics of China, China s Gini coefficient has been over 0.45 since 2000, exceeding the international warning line of 0.4. Income of urban residents has becoming more and more polarized. For example, in 2000 the annual disposable income per capita of the highest income group of Chinese residents is 3.66 times that of people in the lowest income group, and it expanded to 5.54 times in 2010. Because of their relatively low income, the urban low-income life quality is usually unsatisfactory, and their daily travel needs often cannot be guaranteed. Furthermore, the rapid growth of housing price in Chinese cities forces low-income residents to migrate to peripheral settlements on the edge of the city where public transport services are inadequately served. The low-income people are facing more and more travel difficulties. What are the travel needs of urban low-income and how to make them travel conveniently are worth to be studied in depth.
The primary objective of this study is to analyze the travel behavior of urban low-income. More specifically, the study includes the following tasks: (1) to identify the travel data of low-income in the travel database of all populations; (2) to analyze travel characteristics and trip chain characteristics of the urban low-income; (3) to make some policy implications.
2. Literature Review
Department of Housing and Urban Development s (HUD) low-income definition refers to approximately 80 percent of the region s median household income (for more details see: HUD Notice PDR-95-06). As for Bureau of the Census (2001), low-income households are defined as 1 or 2 person household earning under $10,000; 3 or 4 persons earning under $15,000 and more person households earning under $25,000. National Bureau of Statistics of China divides all households into seven categories by income. In turn, 10%, 10%, 20%, 20%, 20%, 10%, and 10% of all households are called the highest income, high income, above middle income, middle income, below middle income, low income, and the lowest income.
In terms of travel behavior of the low-income, studies (1997, 2001, 2005) find that the low-income have lower mobility than the non-low-income based on data from the 1995 Nationwide Personal Transportation Survey (NPTS). The low-income make fewer trips and travel fewer miles than the non-low-income (1994, 1997). Low-income people mainly rely on public transport services and travel on foot and by bicycle to meet their mobility requirements (2004, 2006). Srinivasan and Rogers (2003) indicate that differences in accessibility do affect travel behavior by studying two contrasting locations in the city of Chennai, India. Using data from the 1995 American Travel Survey (ATS), Mallett et al. (2000, 2001) analyze long-distance travel characteristics of the low-income population. It shows that the ability of people in low-income households to travel long distance is quite limited. They travel mainly to visit friends and relatives and on personal business, they rarely travel for leisure or on business.
Public transport is the main motorized travel mode of low-income people. His-Hwa Hu and Kyoung Lee (2001) show that two factors affect low-income people s travel by bus: the traditional idea that only the poor travel by bus and the inconvenience of bus service. Sun, Xu and Chen (2008) indicate that not only inconvenient service but also high bus fares have impact on low- s travel by bus. Astrop et al. (1996, 1997) demonstrate that the provision of public transport services in developing countries is not always able to meet the needs of residents of low income areas by studying two cities of Accra and Pune as examples. In order to improve the mobility of the urban low-income, Peng (2004) points out policy makers should create a good and friendly environment for pedestrian and bicycle and at the same time give the urban low-income more transportation assistance to use public transportation. Other researchers (2006, 2008) propose to increase
accessibility of bus service, improve accessibility of travel information and induce travel cost. Fixed-route transit services. For those living outside the central parts of the city, other forms of mass transportation more suited to low density environment should be explored, such as jitney type services and various types of car sharing arrangements.
On the basis of previous studies, researchers have gotten some conclusions about travel behavior of the low-income in developed countries, but few studies have been conducted in developing countries, especially in China. Income is regarded to be somewhat private in China. Therefore, it is difficult to obtain accurate income data. On the other hand, Chinese government does not establish the official standard of low-income people. In this paper, a method of Two-Step Clustering Analysis is used to identify the low-income people. The findings of this paper will provide useful insights and contribute to the identification of the low-income in the dataset of all people and help understand travel behavior of urban low-income in China.
The city selected for this study is Huzhou, a prefecture-level city located in the northern Zhejiang province of Eastern China, next to Shanghai in the east. The city has a population of 2,600,000, and the area is 1567km2 with
a central district of 95km2. To draft a comprehensive public transportation plan for this city, a traditional travel
diary survey of 8556 individuals above 6 years old in 3024 households is conducted on Wednesday October 19, 2011. The valid samples are 8324 individuals in 2941 households. The survey had three distinct sections: household characteristics, individual sociodemographics, and travel attributes.
(1) Household characteristics. Questions in this section are designed to obtain socioeconomic information about the household. Relevant questions are residential location, household size, number of household members with jobs, number of children under school age, number of household vehicles (including cars, motorcycles, and bicycles), and annual household income range.
(2) Individual sociodemographics. This part includes questions designed to classify the household members according to the following aspects: gender, occupation, age, education level, possession of driving license and possession of IC card.
(3) Travel activity attributes. This section of the survey aims at detecting and characterizing all trips made by household members. The basic variables are trip starting and ending time, origin and destination, mode used, and trip purpose. Trip purpose is divided into nine categories, including work, school, bureaucracy (for government business), shopping, social-recreation, serving passengers, returning home, returning to work and others (besides eight categories mentioned above).
Using data from the survey, travel database of all people including the low-income and other people is established. However, in order to analyze the travel behavior of the low-income, the first step is to identify the low-income group from the dataset.
In this study, Two-Step Clustering Analysis is utilized to classify people in the dataset into different groups. It is briefly discussed as follows:
Two-Step Clustering handles large datasets and continuous and categorical variables. Different from k-means Clustering, there is no need to know the numbers of clusters in advance. The method can automatically choose the most desirable number of clusters based on the basis of statistical evaluation criteria.
In the first step, cases are assigned into pre-clusters and these pre-clusters are treated as single cases in the second step. Then, the two-step procedure conducts a modified hierarchical agglomerative clustering procedure that combines the objects sequentially to form homogenous clusters. This is done by building a so-called cluster
the dataset. The procedure can handle categorical and continuous variables simultaneously and allow the technique to automatically choose the number of clusters on the basis of statistical evaluation criteria. Likewise, the procedure guides the decision of how many clusters to retain from the data by calculating
measures-of-Information Criterion (BIC). Furthermore, the proc importance for the construction of a specific cluster. These desirable features make the somewhat less popular two-step clustering a viable alternative to the traditional methods.
5. Data Analysis and Results
5.1. Clustering Results
Household size and annual household income range can be obtained from the travel survey. There are six categories in annual household income: less than 10,000, 10,000- 20,000, 20,000- 40,000,
40,000-60,000, 60,000- 80,000, and more than 80,000. In order to facilitate data reduction and processing, median value of each income range is used. That is to say, 5,000, 15,000, 30,000, 50,000, 70,000, 100,000 stands for each category respectively. Household size divided by median income is individual annual income. Then two-step clustering analysis is made to classify people into different groups. Results are shown in Table 1.
Table 1. People Classification Results by Two-Step Clustering
Group 1 2 3 4 5
Number of Samples 1687 3122 1911 1370 234
Percentage 20.3% 37.5% 23.0% 16.5% 2.8%
Average Individual Annual Income 8,132 16,400 24,282 33,460 55,086
People in the dataset are classified into five groups. It is clearly can be seen that people in group 1 have the lowest average individual annual income. In this paper, group 1 is defined as low-income group. Other four groups are defined as non-low-income groups.
5.2. Sociodemographic Characteristics
This section provides a brief overview of the socioeconomic and demographic characteristics of urban low-income (Table 2). According to clustering results, there are 1687 low low-income samples in the dataset (about 20.3% of all people). Women are more than men, accounting for 52.87%. With respect to occupation, more than 20% are the retired people and another 16.18% being workers. The largest proportion of the low income is in the range of 60 years and over (23.71%). The range of 40-49 years comes the second. It is seen explicitly that majority of the people just obtain education level of high school or even below. None of low income samples have master degree or above.
Table 2. Key Sociodemographic Characteristics of Low Income People Frequencies Percentage (%) Gender Male 795 47.13 Female 892 52.87 Occupation Student 216 12.80 Worker 273 16.18 Common staff 233 13.81 Management staff 38 2.25 Government staff 44 2.61
Small workshop owner 155 9.19
The retired 357 21.16 Housewife/househusband 183 10.85 Peasant 19 1.13 Others 169 10.02 Age 6-14 years 107 6.34 15-19 years 93 5.51 20-24 years 92 5.45 25-29 years 100 5.93 30-39 years 212 12.57 40-49 years 375 22.23 50-59 years 308 18.26
60 years and over 400 23.71
Middle school or below 1002 59.40
High school 484 28.69
Bachelor degree 201 11.91
Master degree or above 0 0
5.3. Travel Characteristics
According to the survey, there are 1687 low-income samples with 4042 trips, and 6637 non-low-income samples with 16988 trips. Table 3 provides a comparison of key descriptive statistics pertaining to people s travel characteristics. The low-income people have lower mobility than the non-low-income. They make fewer trips and shorter distance. The average trip frequency of low-income people is 2.40 times per day, fewer than that of income people whose is 2.56 times. Moreover, the income make shorter distance than the low-income (6.55km versus 7.40km). The main trip distance of low-low-income lies in less than 3km (37.04%), while non-low-income lies in 3-7km (30.78%). Even though the non-low-income travel fewer kilometers, their average trip duration is longer than the non-low-income. It is possibly that the low-income travel inconveniently and need more time.
(1) Trip Mode
The low-income mainly rely on low cost travel mode, such as walking, electric motorcycle and bicycle (totally 77.96%). However, for the non-low-income, the major mode is electric motorcycle, walk and private cars (totally 74.42%). The predominant motorized mode of low-income people is bus, while for the non-low-income it is private cars. More specifically, 18.34% of all trips are made by private cars for the non-low-income, compared to 4.40% for the low-income.
Table 3. Comparison of Key Travel Characteristics of Low-income and Non-low-income
Average Trip Frequency 2.40 times 2.56 times
Average Trip Duration 21.60min 21.24min
Average Trip Distance 6.55km 7.40km
Less than 3km 37.04% 29.44%
3km-7km 29.61% 30.78%
7km-10km 13.46% 17.10%
More than 10km 19.89% 22.67%
Trip Mode Distribution
Walk 32.74% 24.60% Bicycle 14.01% 9.31% Electric motorcycle 31.21% 31.48% Motorcycle 5.94% 4.93% Bus 5.84% 5.61% Taxi 0.59% 0.74% Tricycle 1.41% 1.15% Private car 4.40% 18.34% Company vehicle 2.67% 2.79% Others 1.19% 1.06% (2) Trip Purpose
Trip purpose distribution of low-income and non-low-income is shown in Table 4 and Fig. 1. Among these trip purposes, work, school, and bureaucracy are subsistence activity. Shopping, serving passengers are called maintenance activities. Social-recreation is called discretionary activity (Yang et al., 2008). Subsistence activities made by low-income people are 25.69%, which is more than non-low-income people with 23.64%. On the other hand, the low-income make fewer maintenance and discretionary activities than the non-low-income. The outcome may result from the low salary and budgeting life of the low-income.
Table 4. Trip Purpose Distribution of Low-income and Non-low-income
Low-Income Non-Low-Income Subsistence Activity Work 19.75% 18.70% School 5.37% 3.96% Bureaucracy 0.57% 0.98% Maintenance Activity Shopping 12.05% 12.62% Serving passengers 3.41% 3.81% Discretionary Activity Social-recreation 5.77% 6.33% Others Returning home 46.80% 46.14% Returning to work 0.79% 1.52% Other purpose 5.49% 5.93%
Fig. 1. Trip Purpose Distribution of Low-income and Non-low-income (3) Trip Time Distribution
Fig.2 provides a comparison of travel starting time distribution of the low-income and the non-low-income. Similarly, starting time presents two-peak shape for both groups, which is morning peak and evening peak. Meanwhile, there are two small peaks at noon. But interestingly, travel percentage at noontime of non-low-income people is somewhat larger than that of low-non-low-income people. It is quite possibly because some low-non-low-income people do not come back home to have lunch.
Fig. 2. Travel Starting Time Distribution of Low-income and Non-low-income (4) Trip Mode Variation by Purpose
In order to shed additional light on the relationship between mode choice and travel purpose of urban low-income, another table has been included in this study. Table 5 shows how trip mode of urban low-income changes by different trip purposes. The table reveals some interesting differences across trip purposes. For example, in terms of subsistence activity, electric motorcycle dominates the whole mode distribution, with the rate up to 41.04%. However, when it comes to maintenance and discretionary activities, walk has the dominate mode share, with percentage of 49.36% and 73.82% respectively. This phenomenon indicates that the low-income tend to travel by electric motorcycle when their time schedule is tight (e.g. go to work or school). But when the time schedule is not tight (e.g. shopping and social recreation), they are more willing to travel on foot.
Table 5. Trip Mode Variation of Low-Income by Purpose
Subsistence Activity Maintenance Activity Discretionary Activity Travel Mode Walk 14.55% 49.36% 73.82% Bicycle 15.32% 15.65% 6.01% Electric motorcycle 41.04% 22.68% 9.87% Motorcycle 8.77% 3.35% 3.00% Bus 6.17% 4.95% 3.86% Taxi 0.29% 0.64% 0.00% Tricycle 0.96% 0.80% 0.86% Private car 6.17% 1.92% 2.15% Company vehicle 4.91% 0.32% 0.00% Others 1.83% 0.32% 0.43%
(5) Travel Characteristics by Age
To understand different travel characteristics of the low-income across age groups, a comparison of
characteristics by age is tabulated (Table 6). The age group of 60 years and over on average make the most trips per day because of their more disposal time. Moreover, the trip purpose distribution reveals that people of 60 years and over make the most maintenance activities and discretionary activities. For the group of 6-24years and 25-59years, subsistence activities dominate their travel purpose distribution, with percentage of 46.03% and 29.44% respectively. When it comes to mode distribution, people between 25-59 years make more than 40% trips by the electric motorcycle. And the majority of trip mode by group of 60 years and over is walking. Interestingly, with respect to travel by bus, people between 6 and 24 years has the largest mode share (12.48%), 60 years and overcoming the next, and 25-59 years the least mode share (3.75%).
Table 6. Travel Characteristics of Urban Low-Income by Age
Characteristic Age Group 6-24 years 25-59 years 60 years and over
Average Travel Frequency 2.11 times 2.44 times 2.48 times
Trip Purpose Distribution
Subsistence Activity 46.03% 29.44% 3.92%
Maintenance Activity 2.76% 13.54% 28.11%
Discretionary Activity 0.97% 3.66% 13.86%
Others 50.24% 53.36% 54.12%
Trip Mode Distribution
Walk 27.88% 21.90% 62.25% Bicycle 23.18% 12.06% 13.05% Electric motorcycle 24.64% 40.88% 11.65% Motorcycle 2.11% 8.60% 1.81% Bus 12.48% 3.75% 6.83% Taxi 0.32% 0.74% 0.40% Tricycle 0.81% 1.11% 2.51% Private car 4.05% 5.89% 1.00% Company vehicle 0.32% 4.36% 0 Others 4.21% .070% 0.50%
5.4. Trip Chain Characteristics
Trip chain is defined as a closed chain, which begins with home as origin and also ends with home as finalff destination after a number of sequential trips (Wang et al., 2002). A simple chain consists of only two trips. And a complex chain is made up of more than two trips. This subsection describes trip chain characteristics from the aspects of quantity, and length. Results are shown in Table 7.
Table 7. Trip Chain Characteristics of Low-income and Non-low-income
Number of Trip Chain per day 1.12 1.18
Average Length of Trip Chain 2.14 2.17
Simple chain 1722(91.06%) 6908(88.12%)
Complex chain 169(8.94%) 931(11.88%)
(1) The Number of Trip Chain
Similarly, both low-income people and non-low-income people make a lot of one trip chains per day (Seen in Fig. 3). However, the average number of trip chain of the non-low-income is more than that of the low-income (1.18 per day versus 1.12 per day). Low-income people are restrained from economic and traffic conditions. Therefore, they seldom make additional trips except some necessary ones (for example, go to school or work).
Fig. 3. Trip Chain Quantity Distribution of Low-income per day (2) Length of Trip Chain
Generally speaking, the majority of trip chains for both groups are simple chains (Seen in Table 7). However, the percentage of low-income people is a little bit larger than that of non-low-income people (91.06% versus 88.12%). Length of trip chain for the low-income is shorter than that of the non-low-income. The length is 2.14 for the low-income, compared to 2.17 for the non-low-income. It means that trip purpose of low-income people is simple and straight every time, while the non-low-income is more complex and flexible.
5.5. Policy Implications
Based on the travel characteristics of urban low-income in China, here are some policy implications recommended aiming at improving travel conditions for them.
(1) City Spatial Structure
From above analysis, the low-income mainly rely on walking, electric motorcycle and bicycle to travel. Their trip duration is usually longer than that of non-low-income people. Under single-center city spatial structure layout, rising housing prices downtown forces the low-income migrate to peripheral settlements on the edge of the city where travel conditions are inconvenient. Thus, cities in China are better to adopt multi-centers spatial structure layout. At the same time, land should be multiple used and developed to decrease long distance trips across regions. Furthermore, bring jobs to the areas with a concentration of the urban low-income. Convenient environment should be created for the low-income to live, work and travel.
(2) Public Transport Service
The most important motorized travel mode for the low-income is public transport. First of all, differentiated bus service should be provided. Basic available bus service should be developed for the low-income. The non-low-income need more comfort and convenient bus service besides available service. Strategies like opening up new bus routes in the accumulation area of low-income people, and construction of supporting bus station and transfer hubs should be made by policy makers. At the same time, in order to decrease travel cost of the low-income, buses without air-conditioners are in priority because of the low fares in low-income community. Local government should increase investment in urban public transit which may include extending service hours, experimenting with non-fixed route service to large residential areas accumulated by the low-income outside of the downtown area.
(3) Slow Traffic Design
Large proportions of trips of low-income people are made by slow traffic, such as walking and bicycle. Experts and officials should build enough walking streets and non-motorized lanes in road network planning and construction, aiming to guarantee the separation of people and vehicles. And more attention should be paid to the construction of barrier-free facilities to make pedestrians safer.
On the other hand, public bicycle system should also be taken into consideration. Parking spots in the low-income community are in priority. Public bicycle system not only facilitates the low-low-income to travel, but also to some extent solves the the last mile problem of public transport service.
(4) Travel Subsidies Strategy
Currently, there are some discounts for students and old people when they take bus in China. In order to improve the mobility of the low-income, policy makers should also take the low-income into subsidy inclusion. For example, low-income people can get some extra money from the government at regular time or they enjoy discounts when using public transport.
6. Conclusions and Discussions
The study analyzes the travel behavior of the urban low-income in China. Two-Step Clustering Analysis is utilized to identify the low-income people from the dataset. Then comparisons between the low-income and the non-low-income are conducted to make more detailed analysis. Based on the outcome of travel characteristics and trip chain characteristics, the following results are made:
(1) Urban low-income people in China generally have lower mobility than the non-low-income. They make fewer trips and shorter distance. However, trip duration of the low-income is longer than that of the non-income. Most of trips by the income are of short distance (less than 3km). The major trip mode of the low-income is walking, electric motorcycle, and bicycle. And bus is the main motorized trip mode. With respects to trip purpose, in low-income people the major purpose is for subsistence activities, with the rate up to 25.69%.
Their maintenance activities and discretionary activities are much less than those of the non-low-income. Interestingly, travel percentage at noontime of low-income people is somewhat smaller than that of non-low-income people.
(2) Subsistence activities of the low-income are associated with higher percentage of electric motorcycle trip engagement. On the other hand, maintenance activities and discretionary activities are mostly made by walking.
There is an increase in average trip frequencies with age. The elder people of the low-income tend to make more trips. With respect to trip purpose distribution, subsistence activities dominate the group of 6-24years and 25-59years, while maintenance activities have the largest percentage in group of 60 years and over. People of 6-24 years and 60 years and over make most of their trips by walking, compared to people of 25-59 years by electric motorcycle.
(3) Similarly, both low-income people and non-low-income people make a lot of one trip chains per day. However, the average number of trip chain of the non-low-income is more than that of the low-income (1.18 per day versus 1.12 per day). Length of trip chain for the low-income is shorter than that of the non-low-income. The length is 2.14 for the low-income, compared to 2.17 for the non-low-income.
(4) To improve travel environment and better meet travel needs of the low-income, some policy implications are made, including multi-centers city spatial structure layout, differentiated and flexible public transport service, friendly and people-oriented slow traffic design, and more travel subsidies.
The findings related to the travel behavior of the urban low-income are very significant in the context of few researches in China and providing transportation equity for the low-income. This study makes an overall analysis of travel behavior of the low-income. Thus, the impact of location conditions on travel behavior may be different. For example, travel behaviors of people who live closer to better transit service and people who don t do not perform in the same way. Further research is needed to establish the relationship between location conditions and travel behavior.
This research is sponsored by the National Natural Science Foundation of China (51178109), National Basic Research 973 Program (2012CB725402). The authors also would like to thank the graduate research assistants at the School of Transportation at the Southeast University for their assistance in data reduction.
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